Essedum 1.0 Accelerates AI Integration for Networking Teams with Open-Source Platform

The Open-Source Platform from LF Networking Accelerates AI in Networking Applications

Earlier this year, Infosys helped launch the Essedum open-source networking project under the Linux Foundation’s LF Networking (LFN) division. This week, Essedum reached a key milestone with its 1.0 release.

Essedum 1.0 is an open-source platform built specifically to speed up AI integration within networking environments. It offers a comprehensive framework covering data ingestion, pipeline orchestration, and model deployment across both on-premises and cloud setups. Besides Infosys’s contributions, the platform incorporates components from the LFN AI Task Force’s Data Sharing Platform.

Seven Core Technical Capabilities in Essedum 1.0

• Connections: Secure communication links between software systems to enable seamless data exchange and integration.
• Datasets: Ingest and manage data from diverse sources, including storage buckets, MySQL databases, and REST APIs.
• Pipelines: Build and manage AI/ML workflows for training, fine-tuning, and inferencing models.
• Models: Access and manage AI models across platforms like AWS SageMaker, Azure ML, and GCP Vertex AI.
• Endpoints: Centralized interface to view and manage all connected endpoints, including REST APIs and model services.
• Adapters: Simplify integration with external services without complex host configurations.
• Remote Executor: Execute pipelines or programs on remote servers or virtual machines to optimize compute-intensive tasks.

Why Essedum Matters for AI Networking Applications

Building AI-powered networking applications usually involves piecing together various tools, which can be complex and time-consuming. General-purpose machine learning platforms like MLflow and Kubeflow handle lifecycle management but lack networking-specific components essential for telecom and network management.

Essedum fills this gap by acting as a specialized integration framework. It doesn’t replace existing AI/ML platforms but works alongside them, enabling organizations to leverage current investments while adding networking-specific features those platforms don’t provide.

Essedum offers:

• Easy Access to AI Building Blocks: It brings together data sharing tools, domain-specific AI models, and application-building frameworks in one place. This setup eliminates the need for teams to individually source and integrate components.
• Reduced Development Time: Pre-built tools and libraries help networking teams develop AI solutions faster, focusing on solving specific problems instead of foundational engineering.

This approach accelerates innovation and helps deliver practical value more quickly.

Production-Ready Sandbox Environment Validates Deployment

Beyond releasing the code, LF Networking partnered with the University of New Hampshire Interoperability Lab to deploy Essedum in a fully operational developer sandbox. This environment lets developers test the platform under real-world conditions, demonstrating its reliability across different infrastructures.

The sandbox also validates Essedum’s ability to deploy consistently in multi-cloud environments, meeting the performance and functionality requirements networking teams need for production use.

Community-Driven Roadmap Targets Operational Improvements

The development of Essedum emphasizes community involvement. The 18-month roadmap focuses on evolving the platform from a foundational release to a tool that changes how network operations teams work daily.

Key priorities include onboarding developers and enriching the community to foster contributions. This collaborative approach ensures that the platform adapts to diverse user needs across various networking environments and organizational structures.

• Docker and Helm-based deployment automation for streamlined cloud-native and containerized environments.
• Support for ingesting PDF and Excel files, common in network planning and operations documentation.
• Secrets management to secure credential handling in enterprise settings.
• Enhanced role-based access control to support multi-team deployments.
• Expanded compatibility with public cloud platforms to fit diverse infrastructure stacks.

As more real-world applications are built on Essedum, continuous feedback will guide improvements and new features, keeping the platform relevant and effective for network operations teams.